Literature DB >> 35710684

Risk prediction of the metabolic syndrome using TyG Index and SNPs: a 10-year longitudinal prospective cohort study.

Sang Wook Kang1, Su Kang Kim2, Young Sik Kim3, Min-Su Park4.   

Abstract

TyG (triglyceride and glucose) index using triglyceride and fasting blood glucose is recommended as a useful marker for insulin resistance. To clarify whether the TyG index is a marker for predicting metabolic syndrome (MetS) and to investigate the importance of single-nucleotide polymorphisms (SNPs) in MetS diagnosis. From 2001 to 2014, a longitudinal prospective cohort study of 3580 adults aged 40-70 years was conducted. The area under the receiver operating characteristic curves (AUROC) and Youden index (YI) was calculated to assess the diagnostic value. During the 14-year follow-up, 1270 subjects developed MetS. Five SNPs in four genes (BUD13 rs10790162, ZPR1 rs2075290, APOA5 rs2266788, APOA5 rs2075291, and MKL1 rs4507196) significantly correlated with susceptibility to MetS (p < 0.00005). The areas under the curve of TyG index and HOMA-IR were 0.854 (95% confidence interval [CI], 0.841-0.867) and 0.702 (95% CI, 0.684-0.721), respectively. Despite no statistical significance, AUROC and YI were increased when MetS was diagnosed using TyG index and the five SNPs. TyG index might be useful for identifying individuals at high risk of developing MetS. The combination of TyG index and SNPs showed better diagnostic accuracy than TyG index alone, indicating the potential value of novel SNPs for MetS diagnosis.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Metabolic syndrome; Single-nucleotide polymorphism; Triglyceride glucose index

Year:  2022        PMID: 35710684     DOI: 10.1007/s11010-022-04494-1

Source DB:  PubMed          Journal:  Mol Cell Biochem        ISSN: 0300-8177            Impact factor:   3.396


  14 in total

1.  The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp.

Authors:  Fernando Guerrero-Romero; Luis E Simental-Mendía; Manuel González-Ortiz; Esperanza Martínez-Abundis; María G Ramos-Zavala; Sandra O Hernández-González; Omar Jacques-Camarena; Martha Rodríguez-Morán
Journal:  J Clin Endocrinol Metab       Date:  2010-05-19       Impact factor: 5.958

2.  Genome-wide screen for metabolic syndrome susceptibility Loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits.

Authors:  Kati Kristiansson; Markus Perola; Emmi Tikkanen; Johannes Kettunen; Ida Surakka; Aki S Havulinna; Alena Stancáková; Chris Barnes; Elisabeth Widen; Eero Kajantie; Johan G Eriksson; Jorma Viikari; Mika Kähönen; Terho Lehtimäki; Olli T Raitakari; Anna-Liisa Hartikainen; Aimo Ruokonen; Anneli Pouta; Antti Jula; Antti J Kangas; Pasi Soininen; Mika Ala-Korpela; Satu Männistö; Pekka Jousilahti; Lori L Bonnycastle; Marjo-Riitta Järvelin; Johanna Kuusisto; Francis S Collins; Markku Laakso; Matthew E Hurles; Aarno Palotie; Leena Peltonen; Samuli Ripatti; Veikko Salomaa
Journal:  Circ Cardiovasc Genet       Date:  2012-03-07

3.  Metabolic syndrome and risk of cardiovascular disease: a meta-analysis.

Authors:  Andrea Galassi; Kristi Reynolds; Jiang He
Journal:  Am J Med       Date:  2006-10       Impact factor: 4.965

4.  Triglycerides and glucose index: a useful indicator of insulin resistance.

Authors:  Gisela Unger; Silvia Fabiana Benozzi; Fernando Perruzza; Graciela Laura Pennacchiotti
Journal:  Endocrinol Nutr       Date:  2014-08-28

5.  The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects.

Authors:  Luis E Simental-Mendía; Martha Rodríguez-Morán; Fernando Guerrero-Romero
Journal:  Metab Syndr Relat Disord       Date:  2008-12       Impact factor: 1.894

6.  A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium.

Authors:  Aldi T Kraja; Dhananjay Vaidya; James S Pankow; Mark O Goodarzi; Themistocles L Assimes; Iftikhar J Kullo; Ulla Sovio; Rasika A Mathias; Yan V Sun; Nora Franceschini; Devin Absher; Guo Li; Qunyuan Zhang; Mary F Feitosa; Nicole L Glazer; Talin Haritunians; Anna-Liisa Hartikainen; Joshua W Knowles; Kari E North; Carlos Iribarren; Brian Kral; Lisa Yanek; Paul F O'Reilly; Mark I McCarthy; Cashell Jaquish; David J Couper; Aravinda Chakravarti; Bruce M Psaty; Lewis C Becker; Michael A Province; Eric Boerwinkle; Thomas Quertermous; Leena Palotie; Marjo-Riitta Jarvelin; Diane M Becker; Sharon L R Kardia; Jerome I Rotter; Yii-Der Ida Chen; Ingrid B Borecki
Journal:  Diabetes       Date:  2011-03-08       Impact factor: 9.461

7.  A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains.

Authors:  Christy L Avery; Qianchuan He; Kari E North; Jose L Ambite; Eric Boerwinkle; Myriam Fornage; Lucia A Hindorff; Charles Kooperberg; James B Meigs; James S Pankow; Sarah A Pendergrass; Bruce M Psaty; Marylyn D Ritchie; Jerome I Rotter; Kent D Taylor; Lynne R Wilkens; Gerardo Heiss; Dan Yu Lin
Journal:  PLoS Genet       Date:  2011-10-13       Impact factor: 5.917

8.  Association and interaction of APOA5, BUD13, CETP, LIPA and health-related behavior with metabolic syndrome in a Taiwanese population.

Authors:  Eugene Lin; Po-Hsiu Kuo; Yu-Li Liu; Albert C Yang; Chung-Feng Kao; Shih-Jen Tsai
Journal:  Sci Rep       Date:  2016-11-09       Impact factor: 4.379

9.  Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance.

Authors:  Sikandar Hayat Khan; Farah Sobia; Najmusaqib Khan Niazi; Syed Mohsin Manzoor; Nadeem Fazal; Fowad Ahmad
Journal:  Diabetol Metab Syndr       Date:  2018-10-05       Impact factor: 3.320

Review 10.  Metabolic syndrome and incident diabetes: current state of the evidence.

Authors:  Earl S Ford; Chaoyang Li; Naveed Sattar
Journal:  Diabetes Care       Date:  2008-06-30       Impact factor: 19.112

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